Neural Network Models for Traffic Noise Quality Prediction: A Comparative Study

Abstract

Problem statement: In the absence of long-range strategic plans, the urban infrastructural growth in Kuwait has been accompanied with significant adverse impacts on the urban environment, and has resulted in the deterioration of the quality of urban life. With this continuous growth, a growing percentage of urban population will be adversely affected by traffic noise pollution. Approach: Traffic-generated noise pollution was monitored at nearly four roadway locations in four districts in metropolitan Kuwait in 2007-2008. At each district, a sample of freeway, arterial, collector, and local residential streets were included in the noise and traffic flow monitoring plan. In addition to the analysis of noise, flow, and their interrelationships, three models – two neural network models and one regression model, were employed to predict traffic noise pollutions. Results: Five uncorrelated components of the noise pollution were used as the ANN model input to predict noise pollution using a back propagation neural network (BPNN), general regression neural network (GRNN) algorithm and a general regression model. The model inputs were the number of vehicles, the equivalent number of cars per hour, the heavy vehicle percentage, the width of road and the average height of buildings facing the road. The models optimum architectures were determined for BPNN model by varying the number of hidden layers, hidden transfer function, test set size percentages, and initial weights. Conclusion: Findings indicate that traffic noise is at or above, the standard outdoor limits in most locations, and especially at arterial roadways and freeways. Comparison of the two prediction results showed that GRNN had the ability to calibrate the multi-component traffic noise and yield reliable results close to that by direct measurements. It was concluded that the optimal BPNN model used in this study provided reasonable predictions of noise profiles for all the data sets employed in this study, with two parallel hidden layer back-propagation showing the best overall prediction. This research has demonstrated the great potential of GRNN modeling technique over BPNN techniques in predicting traffic noise.